

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowercase			:				Union[str, Any]   =     {
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 10_00,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

lowercase			:				List[Any]   =     {
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 10_00,
    "block_out_channels": [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

lowercase			:				Union[str, Any]   =     {
    "sample_size": 2_56,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

lowercase			:				List[str]   =     {
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

lowercase			:				Optional[int]   =     {
    "num_train_timesteps": 2_01,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

lowercase			:				List[str]   =     {
    "num_train_timesteps": 1_51,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}






def        snake_case__		(    lowerCamelCase_		):
							if isinstance(lowerCamelCase_     ,				lowerCamelCase_		):
														return v
							if v.lower() in ("yes", "true", "t", "y", "1"):
														return True
							elif v.lower() in ("no", "false", "f", "n", "0"):
														return False
							else:
														raise argparse.ArgumentTypeError('''boolean value expected'''		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_=False		):
							A      :					List[Any]      		=					checkpoint[F'{old_prefix}.in_layers.0.weight']
							A      :					List[Any]      		=					checkpoint[F'{old_prefix}.in_layers.0.bias']
							A      :					Any      		=					checkpoint[F'{old_prefix}.in_layers.2.weight']
							A      :					Optional[int]      		=					checkpoint[F'{old_prefix}.in_layers.2.bias']
							A      :					List[Any]      		=					checkpoint[F'{old_prefix}.emb_layers.1.weight']
							A      :					Tuple      		=					checkpoint[F'{old_prefix}.emb_layers.1.bias']
							A      :					Union[str, Any]      		=					checkpoint[F'{old_prefix}.out_layers.0.weight']
							A      :					Union[str, Any]      		=					checkpoint[F'{old_prefix}.out_layers.0.bias']
							A      :					Any      		=					checkpoint[F'{old_prefix}.out_layers.3.weight']
							A      :					Tuple      		=					checkpoint[F'{old_prefix}.out_layers.3.bias']

							if has_skip:
														A      :					int      		=					checkpoint[F'{old_prefix}.skip_connection.weight']
														A      :					str      		=					checkpoint[F'{old_prefix}.skip_connection.bias']

							return new_checkpoint






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_=None		):
							A		,		A		,		A      :					str      		=					checkpoint[F'{old_prefix}.qkv.weight'].chunk(3     ,				dim=0		)
							A		,		A		,		A      :					Optional[int]      		=					checkpoint[F'{old_prefix}.qkv.bias'].chunk(3     ,				dim=0		)

							A      :					Any      		=					checkpoint[F'{old_prefix}.norm.weight']
							A      :					Dict      		=					checkpoint[F'{old_prefix}.norm.bias']

							A      :					Union[str, Any]      		=					weight_q.squeeze(-1		).squeeze(-1		)
							A      :					Dict      		=					bias_q.squeeze(-1		).squeeze(-1		)
							A      :					Dict      		=					weight_k.squeeze(-1		).squeeze(-1		)
							A      :					List[Any]      		=					bias_k.squeeze(-1		).squeeze(-1		)
							A      :					List[str]      		=					weight_v.squeeze(-1		).squeeze(-1		)
							A      :					Optional[Any]      		=					bias_v.squeeze(-1		).squeeze(-1		)

							A      :					str      		=					(
							    checkpoint[F'{old_prefix}.proj_out.weight'].squeeze(-1		).squeeze(-1		)
							)
							A      :					Dict      		=					checkpoint[F'{old_prefix}.proj_out.bias'].squeeze(-1		).squeeze(-1		)

							return new_checkpoint






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					List[str]      		=					torch.load(lowerCamelCase_     ,				map_location='''cpu'''		)
							A      :					Optional[Any]      		=					{}

							A      :					Optional[int]      		=					checkpoint['''time_embed.0.weight''']
							A      :					Dict      		=					checkpoint['''time_embed.0.bias''']
							A      :					List[Any]      		=					checkpoint['''time_embed.2.weight''']
							A      :					Optional[int]      		=					checkpoint['''time_embed.2.bias''']

							if unet_config["num_class_embeds"] is not None:
														A      :					Tuple      		=					checkpoint['''label_emb.weight''']

							A      :					List[str]      		=					checkpoint['''input_blocks.0.0.weight''']
							A      :					Tuple      		=					checkpoint['''input_blocks.0.0.bias''']

							A      :					Union[str, Any]      		=					unet_config['''down_block_types''']
							A      :					int      		=					unet_config['''layers_per_block''']
							A      :					List[str]      		=					unet_config['''attention_head_dim''']
							A      :					Tuple      		=					unet_config['''block_out_channels''']
							A      :					Any      		=					1
							A      :					Optional[Any]      		=					channels_list[0]

							for i, layer_type in enumerate(lowerCamelCase_		):
														A      :					List[str]      		=					channels_list[i]
														A      :					Union[str, Any]      		=					current_channels != prev_channels
														if layer_type == "ResnetDownsampleBlock2D":
																					for j in range(lowerCamelCase_		):
																												A      :					Dict      		=					F'down_blocks.{i}.resnets.{j}'
																												A      :					Any      		=					F'input_blocks.{current_layer}.0'
																												A      :					Dict      		=					True if j == 0 and downsample_block_has_skip else False
																												A      :					Any      		=					convert_resnet(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				has_skip=lowerCamelCase_		)
																												current_layer += 1

														elif layer_type == "AttnDownBlock2D":
																					for j in range(lowerCamelCase_		):
																												A      :					str      		=					F'down_blocks.{i}.resnets.{j}'
																												A      :					List[str]      		=					F'input_blocks.{current_layer}.0'
																												A      :					List[str]      		=					True if j == 0 and downsample_block_has_skip else False
																												A      :					Optional[Any]      		=					convert_resnet(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				has_skip=lowerCamelCase_		)
																												A      :					Any      		=					F'down_blocks.{i}.attentions.{j}'
																												A      :					Union[str, Any]      		=					F'input_blocks.{current_layer}.1'
																												A      :					List[str]      		=					convert_attention(
																												    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
																												current_layer += 1

														if i != len(lowerCamelCase_		) - 1:
																					A      :					Optional[Any]      		=					F'down_blocks.{i}.downsamplers.0'
																					A      :					Optional[int]      		=					F'input_blocks.{current_layer}.0'
																					A      :					Any      		=					convert_resnet(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
																					current_layer += 1

														A      :					Tuple      		=					current_channels

							# hardcoded the mid-block for now
							A      :					Optional[int]      		=					'''mid_block.resnets.0'''
							A      :					Union[str, Any]      		=					'''middle_block.0'''
							A      :					Any      		=					convert_resnet(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
							A      :					Tuple      		=					'''mid_block.attentions.0'''
							A      :					List[Any]      		=					'''middle_block.1'''
							A      :					Any      		=					convert_attention(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
							A      :					int      		=					'''mid_block.resnets.1'''
							A      :					Dict      		=					'''middle_block.2'''
							A      :					int      		=					convert_resnet(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)

							A      :					Dict      		=					0
							A      :					Union[str, Any]      		=					unet_config['''up_block_types''']

							for i, layer_type in enumerate(lowerCamelCase_		):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1		):
																												A      :					str      		=					F'up_blocks.{i}.resnets.{j}'
																												A      :					List[Any]      		=					F'output_blocks.{current_layer}.0'
																												A      :					Union[str, Any]      		=					convert_resnet(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				has_skip=lowerCamelCase_		)
																												current_layer += 1

																					if i != len(lowerCamelCase_		) - 1:
																												A      :					str      		=					F'up_blocks.{i}.upsamplers.0'
																												A      :					Dict      		=					F'output_blocks.{current_layer-1}.1'
																												A      :					str      		=					convert_resnet(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
														elif layer_type == "AttnUpBlock2D":
																					for j in range(layers_per_block + 1		):
																												A      :					Any      		=					F'up_blocks.{i}.resnets.{j}'
																												A      :					Union[str, Any]      		=					F'output_blocks.{current_layer}.0'
																												A      :					List[Any]      		=					convert_resnet(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				has_skip=lowerCamelCase_		)
																												A      :					Optional[Any]      		=					F'up_blocks.{i}.attentions.{j}'
																												A      :					Union[str, Any]      		=					F'output_blocks.{current_layer}.1'
																												A      :					int      		=					convert_attention(
																												    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
																												current_layer += 1

																					if i != len(lowerCamelCase_		) - 1:
																												A      :					List[Any]      		=					F'up_blocks.{i}.upsamplers.0'
																												A      :					Optional[Any]      		=					F'output_blocks.{current_layer-1}.2'
																												A      :					Any      		=					convert_resnet(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)

							A      :					List[str]      		=					checkpoint['''out.0.weight''']
							A      :					List[str]      		=					checkpoint['''out.0.bias''']
							A      :					List[Any]      		=					checkpoint['''out.2.weight''']
							A      :					Union[str, Any]      		=					checkpoint['''out.2.bias''']

							return new_checkpoint


if __name__ == "__main__":
		lowercase			:				Dict   =     argparse.ArgumentParser()

		parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
		parser.add_argument(
		    "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
		)
		parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

		lowercase			:				List[Any]   =     parser.parse_args()
		lowercase			:				Dict   =     strabool(args.class_cond)

		lowercase			:				int   =     os.path.basename(args.unet_path)
		print(F"Checkpoint: {ckpt_name}")

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				lowercase			:				Any   =     IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				lowercase			:				Tuple   =     LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				lowercase			:				Optional[Any]   =     TEST_UNET_CONFIG
		else:
				raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

		if not args.class_cond:
				lowercase			:				Union[str, Any]   =     None

		lowercase			:				List[Any]   =     con_pt_to_diffuser(args.unet_path, unet_config)

		lowercase			:				Tuple   =     UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				lowercase			:				int   =     CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				lowercase			:				List[Any]   =     CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				lowercase			:				List[Any]   =     CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

		lowercase			:				List[str]   =     CMStochasticIterativeScheduler(**scheduler_config)

		lowercase			:				Any   =     ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)









lowercase			:				Dict   =     [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					0
							while number:
														# Increased Speed Slightly by checking every 5 digits together.
														sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
														number //= 100000

							return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
lowercase			:				list[bool | None]   =     [None] * 10_00_00_00
lowercase			:				int   =     True
lowercase			:				Tuple   =     False






def        snake_case__		(    lowerCamelCase_		):

							if CHAINS[number - 1] is not None:
														return CHAINS[number - 1]  # type: ignore

							A      :					int      		=					chain(next_number(lowerCamelCase_		)		)
							A      :					Dict      		=					number_chain

							while number < 10000000:
														A      :					Any      		=					number_chain
														number *= 10

							return number_chain






def        snake_case__		(    lowerCamelCase_ = 10000000		):
							for i in range(1     ,				lowerCamelCase_		):
														if CHAINS[i] is None:
																					chain(i + 1		)

							return CHAINS[:number].count(lowerCamelCase_		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(F"{solution() = }")






